With the escalating frequency of extreme grid disturbances, such as natural disasters, comes an increasing need for efficient recovery plans. Algorithms for optimal power restoration play an important role in developing such plans, but also give rise to challenging mixed-integer nonlinear optimization problems, where tractable solution methods are not yet available. To assist in research on such solution methods, this work proposes PowerModelsRestoration, a flexible, open-source software framework for rapidly designing and testing power restoration algorithms. PowerModelsRestoration constructs a mathematical modeling layer for formalizing core restoration tasks that can be combined to develop complex workflows and high performance heuristics. The efficacy of the proposed framework is demonstrated by proof-of-concept studies on three established cases from the literature, focusing on single-phase positive sequence network models. The results demonstrate that PowerModelsRestoration reproduces the established literature, and for the first time provide an analysis of restoration with nonlinear power flow models, which have not been previously considered.

中文翻译：

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PowerModelsRestoration.jl：一个用于探索电网恢复算法的开源框架

随着极端电网干扰（例如自然灾害）的频率不断上升，对有效恢复计划的需求也越来越大。优化功率恢复的算法在制定此类计划中发挥着重要作用，但也会引起具有挑战性的混合整数非线性优化问题，在这些问题中，尚无易处理的解决方法。为了协助此类解决方法的研究，这项工作提出了 PowerModelsRestoration，这是一个灵活的开源软件框架，用于快速设计和测试电源恢复算法。PowerModelsRestoration 构建了一个数学建模层，用于形式化核心恢复任务，这些任务可以结合起来开发复杂的工作流程和高性能启发式方法。对文献中三个既定案例的概念验证研究证明了所提出框架的有效性，重点是单相正序网络模型。结果表明，PowerModelsRestoration 重现了既定文献，并首次提供了非线性潮流模型的恢复分析，这是以前没有考虑过的。